Abstract
This paper describes a technique for improving the performance of parallel genetic algorithms on multi-modal numerical optimisation problems. It employs a cluster analysis algorithm to identify regions of the search space in which more than one sub-population is sampling. Overlapping clusters are merged in one sub-population whilst a simple derating function is applied to samples in all other sub-populations to discourage them from further sampling in that region. This approach leads to a better distribution of the search effort across multiple sub-populations and helps to prevent premature convergence. On the test problems used, significant performance improvements over the traditional island model implementation are realised.
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References
Goldberg, D.E.: Genetic algorithms in search, optimisation and machine learning. Addison Wesley, Reading (1989) ISBN 0201157675
Alba, E., Troya, J.M.: A survey of parallel distributed genetic algorithms. Complexity 4(4), Wiley (1999)
Cantú-Paz, E.: Efficient and accurate parallel genetic algorithms. Kluwer, Dordrecht (2000) ISBN 0792372212
Gordan, V.S., Whitley, D.: Serial and parallel genetic algorithms as function optimisers. In: Proc. 5th Int. Conf. on Genetic Algorithms, Morgan Kaufmann, San Francisco (1993)
Vincent, J., King, G.: Performance Implications of Domain Decomposition in the Parallelisation of Genetic Search. In: Proc. Genetic and Evolutionary Computation Conference, Late Breaking Papers, July 2001, pp. 443–449 (2001)
Vincent, J., King, G., Dupée, B.: Dynamic balancing of workload in a parallel genetic algorithm based on domain decomposition. In: Proc. IASTED Int. Conf. on Parallel and Distributed Computing Systems, ACTA Press (2001)
Vincent, J., King, G.: Improving the parallel genetic algorithm with a view to online optimisation. In: Proc. IASTED Int. Conf. on Intelligent Systems and Control, ACTA Press (2001)
Vincent, J., Mintram, R.: On the Scalability of a Parallel Genetic Algorithm Based on Domain Decomposition. In: Proc. IASTED Int. Conf. on Artificial Intelligence and Applications, ACTA Press (2003)
Goldberg, D.E., Kargupta, H., Horn, J., Cantú-Paz, E.: Critical deme size for serial and parallel genetic algorithms. Technical Report IlliGAL 95002, University of Illinois (1995)
Goldberg, D.E., Richardson, J.: Genetic Algorithms with Sharing for Multimodal Function Optimisation. In: Proc. 2nd Inter. Conf. Genetic Algorithms, pp. 41–49 (1987)
Deb, K., Goldberg, D.E.: An Investigation of Niche and Species Formation in Genetic Function Optimisation. In: Proceedings of The Fifth International Conference on Genetic Algorithms and Their Applications, pp. 42–50. Morgan Kaufmann, San Francisco (1989)
Everrit, B.: Cluster Analysis, Edward Arnold (1993) ISBN 0470220430
Eshelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval schemata. In: Foundations of Genetic Algorithms 2, Morgan Kaufmann, San Mateo (1993) ISBN 1558602631
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Yang, Y., Vincent, J., Littlefair, G. (2004). A Coarse-Grained Parallel Genetic Algorithm Employing Cluster Analysis for Multi-modal Numerical Optimisation. In: Liardet, P., Collet, P., Fonlupt, C., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2003. Lecture Notes in Computer Science, vol 2936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24621-3_19
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DOI: https://doi.org/10.1007/978-3-540-24621-3_19
Publisher Name: Springer, Berlin, Heidelberg
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